{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 7. 成交量指标\n",
    "*市场短期是投票机，长期是称重机*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 目录\n",
    "1. 什么是成交量？\n",
    "2. 如何计算成交量指标？\n",
    "3. 如何用图形展示成交量？\n",
    "4. 如何用成交量指标来编写策略？"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 什么是成交量？\n",
    "\n",
    "成交量有三种可能，分别是量增、量减、量平。 \n",
    "\n",
    "价格也有三种可能，分别是上涨、下跌、横盘。\n",
    "\n",
    "量价理论表明价涨量增是上涨趋势的确认，反之价跌量增是下跌趋势的确认。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何计算成交量指标？\n",
    "---\n",
    "### 1. ADV-平均成交量\n",
    "$MA(volume,20)$\n",
    "\n",
    "---\n",
    "\n",
    "### 2. OBV\n",
    "OBV属于市场的领先指标，计算方法是如果当天价格高于前一天的价格，当天成交量就用加的，反之就用减的来计算出OBV指标，这样有助于分析成交量与价格的关系。\n",
    "\n",
    "---\n",
    "\n",
    "### 3. A/D\n",
    "A/D 指标在OBV的基础上对价格的波幅进行了计算，真实波幅越大，指标中的成交量占比越大。\n",
    "\n",
    "$\\frac{A}{D}=\\frac{Close-Open}{High-Low}*Volume$\n",
    "\n",
    "---\n",
    "\n",
    "### 4. WVAP\n",
    "按成交量来衡量价格的权重，成交量越大价格权重越大。\n",
    "\n",
    "$sum(close * volume, 20)/sum(volume, 20)$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何用图形展示成交量？"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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rHzfmptLMwjL+vT6eNQdPk5ZvCSw7ErJZfPtFKKX46XA6f/hoBwC3XtxH9jkR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\nkZudXOpwSH7Ze4ag7Fk0fGMlbSMDaFajMnGJl1i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lC2si8Ja2rQABhVxzC9AadRH0X4Cu\nmns40Bj4HOjjwjp88vjpDvzqwlqG5g/TldNYPj8voA52cVk9QDQwRNvuCHzhAlpaa9dNy+ceTgnf\n/7LQky/c7UC7Yj6bYusp8UvmKj9gGdAZdYJlUJ6HdjCfv6EFJLCTgGcREkBsnv0BwLx8fhaWNIE5\nk4487r+4qpaCwqwIaUxzjwE6u7IeYB8Qqm0LINWZteTzn1aI+02//6WhJ8+xOpo2UZK0Vhw9Fa6J\nLC9CiHCgKbAZqCqlPK0dOgNUvcG5vtrmJK1aGC2EKOicYNRlanIp9SVrnEGHEOI5IcQR1JLT8JLo\n0MIJp/yfSW+tCWCxECKUm8BJ9CCEqAHUBNYUV0O+cMIpXz27gF7adk/AWwjhX1wdWnzCKXstt4yb\n0ZOP/sC3UrMW+SjV/KzCGhghhBfwPfCilDI17zHtxt5o+JwRdXWAGCllM2Aj8F5ZxPV6OIsOKeVs\nKWUE8Cowrrjng9No+REIl1I2An4HFhXz/Ms4iZ5c+gOLpZT2Ep7vLHpeBtoLIXYA7VFX5yi2JifR\nUmqUgp689Ae+LsXoFUqFNDBCCBPqw/iflPIHzTlRCBGkHQ8Czt4gmCQgA8g9PxpoJoQw5OkoexP1\nBcjbEVngkjUVSMc3wEOuqkVKmSSlzNbcPwFuL64WZ9KTh5vKNJxFj5QyQUrZS0rZFPiP5lasgQu3\nWEuZU0p6csOKAoxSyu3afpnmZxXOwGgjHhYAB6SU0/IcWg4M0baHoLZlFopWKvgRuFtz6gTsl1La\npZRNtN/rWjU1VQjRWrv24BuF7Wo6hBCReYK8HzjkwlqC8gTZHThQHC3OpkeLTz2gMmopu9g4kx4h\nRIAQIjdfeg341Jm1FCduJaG09ORhAHkKImWenxWlo8aVfqgjKCSwG9ip/boB/sBq1MxxFeCX55xj\nwAUgDbXNsYHmXgNYp4W1Gggr5JrNgb3AEWAWVyawttDCS0ctEe1zUR0zUDtfdwJrgYYu/Eze0bTs\n0rTUc+U0ph2bAEypIO9MH+16cag1TLMLaJmqnefQ/ifc7PtfFnq0Y0e5QZq/zrMpth59Jr+Ojo6O\nTplQ4ZrIdHR0dHScA93A6Ojo6OiUCbqB0dHR0dEpE3QDo6Ojo6NTJugGRkdHR0enTNANjI6Ojo5O\nmaAbGB0dHR2dMkE3MDo6Ojo6ZcL/AycxTjhKXm40AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x8853dd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#ADV 平均成交量\n",
    "import talib as ta\n",
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = ts.get_k_data('600036', start='2016-01-01', end='2016-12-31', ktype='D', autype='qfq')\n",
    "data.index = pd.to_datetime(data['date'], format='%Y-%m-%d')\n",
    "\n",
    "data['Volume_MA10'] = ta.abstract.MA(data, 10, price='volume')\n",
    "data['Volume_MA20'] = ta.abstract.MA(data, 20, price='volume')\n",
    "\n",
    "plt.subplot(2, 1, 1)\n",
    "plt.plot(data.close, label='600036')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.bar(data.index, data.volume,color='g')\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(data['Volume_MA10'], label='Volume_MA10')\n",
    "plt.plot(data['Volume_MA20'], label='Volume_MA20')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lam4xAC6qqUdRlEZw0ysBKeXUajZ/fZ2yicDYSs83A9d0HzU03bxb8vXMnhXP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\nMZUEFEVRpjGVBBRFUaax/wGsL/T7IyvYZQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbc096a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#OBV & A/D\n",
    "data['OBV'] = ta.abstract.OBV(data)\n",
    "data['AD'] = ta.abstract.AD(data)\n",
    "\n",
    "plt.subplot(3, 1, 1)\n",
    "plt.plot(data.close, label='600036')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(3, 1, 2)\n",
    "plt.plot(data.OBV,'g', label='OBV')\n",
    "plt.legend(loc='upper left')\n",
    "plt.subplot(3, 1, 3)\n",
    "plt.plot(data.AD, 'y', label='A/D')\n",
    "plt.legend(loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ScQDsP1ne4eP+Y9UB7ntvFwCK5vPqmqbx9YF8JqdEEmgyknGyosHz+eW1zd7Y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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xcab0588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#VWAP\n",
    "def ts_sum(ts, window=20):\n",
    "    return ts.rolling(window).sum()\n",
    "\n",
    "data['VWAP'] = ts_sum(data.volume*data.close)/ts_sum(data.volume)\n",
    "\n",
    "plt.plot(data.close)\n",
    "plt.plot(data.VWAP)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 如何用成交量指标来编写策略？\n",
    "### OBV突破策略\n",
    "买入：\n",
    "$OBV_t>High(OBV,20) \\text{ and } Close_t>MA(Close,20)$\n",
    "\n",
    "卖出：\n",
    "$OBV_t<Low(OBV,20) \\text{ or } Close_t< MA(Close,20)$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cX/6u4mtFaStUsG8tKsztYn12Dpti0ukX4lG36WyP/gCrZ0PXSXhM/5IOK27n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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa5232e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import talib as ta\n",
    "import tushare as ts\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "data = ts.get_k_data('600036', start='2016-01-01', end='2016-12-31', ktype='D', autype='qfq')\n",
    "data.index = pd.to_datetime(data['date'], format='%Y-%m-%d')\n",
    "\n",
    "\n",
    "data['OBV'] = ta.abstract.OBV(data)\n",
    "data['OBV_MAX20'] = ta.abstract.MAX(data, 20, price='OBV')\n",
    "data['OBV_MIN20'] = ta.abstract.MIN(data, 20, price='OBV')\n",
    "data['OBV_MID20'] = (data['OBV_MAX20'] + data['OBV_MIN20'])/2\n",
    "data['C_MA'] = ta.abstract.MA(data, 20, price='close')\n",
    "\n",
    "buy_time = []\n",
    "buy_price = []\n",
    "\n",
    "sell_time = []\n",
    "sell_price = []\n",
    "\n",
    "data = data.dropna()\n",
    "it = data.iterrows()\n",
    "t0, d0 = next(it)\n",
    "pos = 0\n",
    "\n",
    "for t1, d1 in it:\n",
    "    if pos==0 and d1.OBV >= d0.OBV_MAX20 and d1.close > d1.C_MA:\n",
    "        buy_time.append(t1)\n",
    "        buy_price.append(d1.close)\n",
    "        pos=1\n",
    "    elif pos==1 and (d1.OBV <= d0.OBV_MIN20 or d1.close < d1.C_MA):\n",
    "        sell_time.append(t1)\n",
    "        sell_price.append(d1.close)\n",
    "        pos=0\n",
    "    t0, d0 = t1, d1\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(data['close'])\n",
    "plt.plot(data['C_MA'])\n",
    "plt.scatter(buy_time, buy_price, c='r', marker='^', linewidths=3)\n",
    "plt.scatter(sell_time, sell_price, c='g', marker='v', linewidths=3)\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(data.OBV)\n",
    "plt.plot(data.OBV_MAX20, 'g', alpha=0.3)\n",
    "plt.plot(data.OBV_MID20, 'r', alpha=0.3)\n",
    "plt.plot(data.OBV_MIN20, 'g', alpha=0.3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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e0esoIiKnROV+grW7Cln8+T7uHNmV2OhIr+OIiJySOpW7mV1iZlvMLNvMHjjJ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\nfHtvQwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc366128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "money = np.array(sell_price) - np.array(buy_price)\n",
    "money = list(money)\n",
    "money.insert(0,0)\n",
    "money = np.array(money)\n",
    "capital = money.cumsum()\n",
    "plt.plot(capital)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 作业\n",
    "参考以上代码用其他成交量指标做策略"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
